Complete Guide to AI and Data Science for SQL Developers From Beginner to Advanced

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01 - Introduction

01 - Masterclass introduction.mp4

00:52

02 - What you should know.mp4

02:45

03 - Using the course exercise files.mp4

02:41

02 - 1. Introduction to Data Science

01 - Data science explained.mp4

04:17

02 - Data or information What's the difference.mp4

02:08

03 - Data's increasing influence.mp4

02:43

04 - Comparing data roles.mp4

05:19

05 - Necessary skills for data science.mp4

04:46

06 - Real-world applications.mp4

05:46

07 - Challenge Data.mp4

00:45

08 - Solution Data.mp4

01:12

03 - 2. AI, ML, and DL

01 - The evolving face of artificial intelligence.mp4

04:13

02 - Machine learning problem classifications.mp4

04:29

03 - Machine learning approaches for business scenarios.mp4

03:26

04 - Challenge AI.mp4

01:26

05 - Solution AI.mp4

00:38

04 - 3. Introduction to Stats and Probability

01 - Statistics defined.mp4

02:38

02 - Statistical mindset.mp4

03:00

03 - Types of statistics.mp4

02:45

04 - Descriptive statistics.mp4

07:30

05 - Inferential statistics (probability).mp4

05:44

06 - Python.mp4

03:58

07 - Challenge Statistics.mp4

01:18

08 - Solution Statistics.mp4

00:40

05 - 4. Predicting House Prices with Linear Regression

01 - Project goal.mp4

03:54

02 - Project steps.mp4

02:25

03 - Python environment setup.mp4

04:28

04 - SQL environment setup.mp4

02:48

05 - Project approach.mp4

03:24

06 - 5. Data Preparation and Exploration

01 - Importing necessary libraries and dataset overview.mp4

03:18

02 - Loading the data.mp4

07:36

03 - Checking the data info.mp4

02:13

04 - Summary statistics of the dataset.mp4

05:49

05 - Checking the distribution of the variables.mp4

05:42

06 - Applying log transformation and re-checking distribution.mp4

03:06

07 - Challenge Preparation.mp4

01:05

08 - Solution Preparation.mp4

01:19

07 - 6. Data Visualization and Exploration

01 - Bivariate analysis Heat-map.mp4

05:23

02 - Visualizing relationships Age of homes and distance to work.mp4

03:52

03 - Visualizing relationships Highway access and property tax.mp4

04:09

04 - Checking correlation after removing outliers.mp4

03:40

05 - Visualizing relationships Other pairs of variables.mp4

05:08

06 - Challenge Visualization.mp4

01:09

07 - Solution Visualization.mp4

00:56

08 - 7. Data Preprocessing

01 - Splitting the dataset into train and test sets.mp4

04:55

02 - Checking for multicollinearity using VIF.mp4

02:52

03 - Removing multicollinearity by dropping the tax feature.mp4

02:58

04 - Challenge Preprocessing.mp4

01:15

05 - Solution Preprocessing.mp4

01:11

09 - 8. Model Building and Evaluation

01 - Creating the linear regression model and model summary Part 1.mp4

09:33

02 - Creating the linear regression model and model summary Part 2.mp4

07:16

03 - Creating the linear regression model and model summary Part 3.mp4

05:33

04 - Dropping insignificant variables and re-creating the model.mp4

07:57

05 - Checking assumptions for linear regression.mp4

03:18

06 - Assumption 1 Checking for mean residuals.mp4

02:47

07 - Assumption 2 Checking homoscedasticity.mp4

03:13

08 - Assumption 3 Checking linearity.mp4

02:12

09 - Assumption 4 Checking normality of error terms.mp4

03:24

10 - Q-Q plot for checking the normality of error terms.mp4

03:14

11 - Model performance comparison on train and test data.mp4

06:07

12 - Applying cross-validation and evaluation.mp4

04:40

13 - Challenge Model building.mp4

00:48

14 - Solution Model building.mp4

01:16

10 - 9. Model Interpretation and Reporting

01 - Extracting and creating a DataFrame of coefficients.mp4

05:53

02 - Writing the linear regression equation and coefficients.mp4

05:24

03 - Conclusions and business recommendations.mp4

04:45

04 - Challenge Interpretation.mp4

00:55

05 - Solution Interpretation.mp4

01:46

11 - 10. Final Capstone Project

01 - Final capstone project details.mp4

01:09

02 - Final capstone project solution walkthrough.mp4

04:08

12 - Conclusion

01 - Next steps.mp4

02:28

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LinkedIn Learning is an American online learning provider. It provides video courses taught by industry experts in software, creative, and business skills. It is a subsidiary of LinkedIn. All the courses on LinkedIn fall into four categories: Business, Creative, Technology and Certifications. It was founded in 1995 by Lynda Weinman as Lynda.com before being acquired by LinkedIn in 2015. Microsoft acquired LinkedIn in December 2016.